With rapid development of the computer technology and continuous reduction of costs of various monitoring devices, video monitoring systems are extensively applied in fields of finance, transportation, military and the like. The technology for detecting and tracking a moving object in a video sequence has been an important research subject in the computer vision field.
In recent years, many scholars have proposed solutions for moving object detection, such as the Gaussian mixture model algorithm (GMM), the codebook algorithm (Codebook), the visual background extraction algorithm (Vibe) and the GMG algorithm. According to the Gaussian mixture model algorithm, multiple independent Gaussian distributions are established for each pixel, thus a moving object in a complex scene can be well extracted. However, this algorithm requires time for training samples. In addition, it is difficult to establish an effective background model using the Gaussian mixture model algorithm in a case that the lighting condition changes abruptly since parameters are fixed. According to the codebook algorithm, a codebook structure is established for each pixel, thereby providing a good real-time performance. However, a large amount of memory is occupied, and the algorithm is susceptible to subtle disturbances in the background. The visual background extraction algorithm adopts a random sample model, thus a complete moving object can be rapidly extracted, and the algorithm has certain immunity to noises. However, the disadvantages of the algorithm includes that, sample values of the background model are repeatedly selected, a fixed segmentation threshold cannot adapt to dynamic change of the background in a complex video scene, and noises caused by changes of the lighting cannot be effectively eliminated using the fixed updating factor. The GMG algorithm is a non-parametric method, which generates a time-varying background model using the Bayesian inference. The algorithm shows a poor performance in a lighting-varying scene.
As can be seen, a process of detecting a moving object in a video monitoring picture according to the conventional technology is relatively cumbersome and a detection effect needs further improvement.
In view of the above, problems to be solved include how to improve the effect of moving object detection and how to reduce the complexity of the detection process.